OpenRaiser/Intern-Atlas huggingface dataset:
Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

The rapid growth of the internet has made personalized recommendation systems indispensable. Graph-based sequential recommendation systems, powered by Graph Neural Networks (GNNs), effectively capture complex user-item interactions but often face challenges such as noise and static representations. In this paper, we introduce the Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation (ALDA4Rec) method, a novel model that constructs an item-item graph, filters noise through community detection, and enriches user-item interactions. Graph Convolutional Networks (GCNs) are then employed to learn short-term representations, while averaging, GRUs, and attention mechanisms are utilized to model long-term embeddings. An MLP-based adaptive weighting strategy is further incorporated to dynamically optimize long-term user preferences. Experiments conducted on four real-world datasets demonstrate that ALDA4Rec outperforms state-of-the-art baselines, delivering notable improvements in both accuracy and robustness. The source code is available at https://github.com/zahraakhlaghi/ALDA4Rec.
Systematic Bernoulli Generator Matrix Codes
Venue: arXiv cs.IT 2025
Year: 2025

This paper is concerned with the systematic Bernoulli generator matrix~(BGM) codes, which have been proved to be capacity-achieving over binary-input output-symmetric~(BIOS) channels in terms of bit-error rate~(BER). We prove that the systematic BGM codes are also capacity-achieving over BIOS channels in terms of frame-error rate (FER). To this end, we present a new framework to prove the coding theorems for binary linear codes. Different from the widely-accepted approach via ensemble enlargement, the proof directly applies to the systematic binary linear codes. The new proof indicates that the pair-wise independence condition is not necessary for proving the binary linear code ensemble to achieve the capacity of the BIOS channel. The Bernoulli parity-check~(BPC) codes, which fall within the framework of the systematic BGM codes with parity-check bits known at the decoder can also be proved to achieve the capacity. The presented framework also reveals a new mechanism pertained to the systematic linear codes that the systematic bits and the corresponding parity-check bits play different roles. Precisely, the noisy systematic bits are used to limit the list size of candidate codewords, while the noisy parity-check bits are used to select from the list the maximum likelihood codeword. For systematic BGM codes with finite length, we derive the lower bounds on the BER and FER, which can be used to predict the error floors. Numerical results show that the systematic BGM codes match well with the derived error floors. The performance in water-fall region can be improved with approaches in statistical physics and the error floors can be significantly improved by implementing the concatenated codes with the systematic BGM codes as the inner codes.
Models, Methods and Waveforms for Estimation and Prediction of Doubly Sparse Time-Varying Channels
Venue: arXiv cs.IT 2025
Year: 2025

This paper investigates channel estimation for linear time-varying (LTV) wireless channels under double sparsity, i.e., sparsity in both the delay and Doppler domains. An on-grid approximation is first considered, enabling rigorous hierarchical-sparsity modeling and compressed sensing-based channel estimation. Guaranteed recovery conditions are provided for affine frequency division multiplexing (AFDM), orthogonal frequency division multiplexing (OFDM) and single-carrier modulation (SCM), highlighting the superiority of AFDM in terms of doubly sparse channel estimation. To address arbitrary Doppler shifts, a relaxed version of the on-grid model is introduced by making use of multiple elementary Expansion Models (BEM) each based on Discrete Prolate Spheroidal Sequences (DPSS). Next, theoretical guarantees are provided for the precision of this off-grid model before further extending it to tackle channel prediction by exploiting the inherent DPSS extrapolation capability. Finally, numerical results are provided to both validate the proposed off-grid model for channel estimation and prediction purposes under the double sparsity assumption and to compare the corresponding mean squared error (MSE) and the overhead performance when the different wireless waveforms are used.
Bake Two Cakes with One Oven: RL for Defusing Popularity Bias and Cold-start in Third-Party Library Recommendations
Venue: arXiv cs.SE 2025
Year: 2025

Third-party libraries (TPLs) have become an integral part of modern software development, enhancing developer productivity and accelerating time-to-market. However, identifying suitable candidates from a rapidly growing and continuously evolving collection of TPLs remains a challenging task. TPL recommender systems have been studied, offering a promising solution to address this issue. They typically rely on collaborative filtering (CF) that exploits a two-dimensional project-library matrix (user-item in general context of recommendation) when making recommendations. We have noticed that CF-based approaches often encounter two challenges: (i) a tendency to recommend popular items more frequently, making them even more dominant, a phenomenon known as popularity bias, and (ii) difficulty in generating recommendations for new users or items due to limited user-item interactions, commonly referred to as the cold-start problem. In this paper, we propose a reinforcement learning (RL)-based approach to address popularity bias and the cold-start problem in TPL recommendation. Our method comprises three key components. First, we utilize a graph convolution network (GCN)-based embedding model to learn user preferences and user-item interactions, allowing us to capture complex relationships within interaction subgraphs and effectively represent new user/item embeddings. Second, we introduce an aggregation operator to generate a representative embedding from user and item embeddings, which is then used to model cold-start users. Finally, we adopt a model-based RL framework for TPL recommendation, where popularity bias is mitigated through a carefully designed reward function and a rarity-based replay buffer partitioning strategy. The results demonstrated that our proposed approach outperforms state-of-the-art models in cold-start scenarios while effectively mitigating the impact of popularity bias.
Beyond Misinformation: A Conceptual Framework for Studying AI Hallucinations in (Science) Communication
Venue: arXiv cs.HC 2025
Year: 2025

This paper proposes a conceptual framework for understanding AI hallucinations as a distinct form of misinformation. While misinformation scholarship has traditionally focused on human intent, generative AI systems now produce false yet plausible outputs absent of such intent. I argue that these AI hallucinations should not be treated merely as technical failures but as communication phenomena with social consequences. Drawing on a supply-and-demand model and the concept of distributed agency, the framework outlines how hallucinations differ from human-generated misinformation in production, perception, and institutional response. I conclude by outlining a research agenda for communication scholars to investigate the emergence, dissemination, and audience reception of hallucinated content, with attention to macro (institutional), meso (group), and micro (individual) levels. This work urges communication researchers to rethink the boundaries of misinformation theory in light of probabilistic, non-human actors increasingly embedded in knowledge production.
Collective Learning Mechanism based Optimal Transport Generative Adversarial Network for Non-parallel Voice Conversion
Venue: arXiv cs.SD 2025
Year: 2025

After demonstrating significant success in image synthesis, Generative Adversarial Network (GAN) models have likewise made significant progress in the field of speech synthesis, leveraging their capacity to adapt the precise distribution of target data through adversarial learning processes. Notably, in the realm of State-Of-The-Art (SOTA) GAN-based Voice Conversion (VC) models, there exists a substantial disparity in naturalness between real and GAN-generated speech samples. Furthermore, while many GAN models currently operate on a single generator discriminator learning approach, optimizing target data distribution is more effectively achievable through a single generator multi-discriminator learning scheme. Hence, this study introduces a novel GAN model named Collective Learning Mechanism-based Optimal Transport GAN (CLOT-GAN) model, incorporating multiple discriminators, including the Deep Convolutional Neural Network (DCNN) model, Vision Transformer (ViT), and conformer. The objective of integrating various discriminators lies in their ability to comprehend the formant distribution of mel-spectrograms, facilitated by a collective learning mechanism. Simultaneously, the inclusion of Optimal Transport (OT) loss aims to precisely bridge the gap between the source and target data distribution, employing the principles of OT theory. The experimental validation on VCC 2018, VCTK, and CMU-Arctic datasets confirms that the CLOT-GAN-VC model outperforms existing VC models in objective and subjective assessments.
Cops and Robbers for Graphs on Surfaces with Crossings
Venue: arXiv cs.DM 2025
Year: 2025

Cops and Robbers is a game played on a graph where a set of cops attempt to capture a single robber. The game proceeds in rounds, where each round first consists of the cops' turn, followed by the robber's turn. In the cops' turn, every cop can choose to either stay on the same vertex or move to an adjacent vertex, and likewise the robber in his turn. The robber is considered to be captured if, at any point in time, there is some cop on the same vertex as the robber. A natural question in this game concerns the cop-number of a graph -- the minimum number of cops needed to capture the robber. It has long been known that graphs embeddable (without crossings) on surfaces of bounded genus have bounded cop-number. In contrast, the class of 1-planar graphs -- graphs that can be drawn on the plane with at most one crossing per edge -- does not have bounded cop-number. This paper initiates an investigation into how distance between crossing pairs of edges influences a graph's cop number. In particular, we look at Distance $d$ Cops and Robbers, a variant of the classical game, where the robber is considered to be captured if there is a cop within distance $d$ of the robber. Let $c_d(G)$ denote the minimum number of cops required in the graph $G$ to capture a robber within distance $d$. We look at various classes of graphs, such as 1-plane graphs, $k$-plane graphs (graphs where each edge is crossed at most $k$ times), and even general graph drawings, and show that if every crossing pair of edges can be connected by a path of small length, then $c_d(G)$ is bounded, for small values of $d$.
Generative AI Act II: Test Time Scaling Drives Cognition Engineering
Venue: arXiv cs.CL 2025
Year: 2025

The first generation of Large Language Models - what might be called "Act I" of generative AI (2020-2023) - achieved remarkable success through massive parameter and data scaling, yet exhibited fundamental limitations such as knowledge latency, shallow reasoning, and constrained cognitive processes. During this era, prompt engineering emerged as our primary interface with AI, enabling dialogue-level communication through natural language. We now witness the emergence of "Act II" (2024-present), where models are transitioning from knowledge-retrieval systems (in latent space) to thought-construction engines through test-time scaling techniques. This new paradigm establishes a mind-level connection with AI through language-based thoughts. In this paper, we clarify the conceptual foundations of cognition engineering and explain why this moment is critical for its development. We systematically break down these advanced approaches through comprehensive tutorials and optimized implementations, democratizing access to cognition engineering and enabling every practitioner to participate in AI's second act. We provide a regularly updated collection of papers on test-time scaling in the GitHub Repository: https://github.com/GAIR-NLP/cognition-engineering
Outlier-Robust Multi-Model Fitting on Quantum Annealers
Venue: arXiv cs.CV 2025
Year: 2025

Multi-model fitting (MMF) presents a significant challenge in Computer Vision, particularly due to its combinatorial nature. While recent advancements in quantum computing offer promise for addressing NP-hard problems, existing quantum-based approaches for model fitting are either limited to a single model or consider multi-model scenarios within outlier-free datasets. This paper introduces a novel approach, the robust quantum multi-model fitting (R-QuMF) algorithm, designed to handle outliers effectively. Our method leverages the intrinsic capabilities of quantum hardware to tackle combinatorial challenges inherent in MMF tasks, and it does not require prior knowledge of the exact number of models, thereby enhancing its practical applicability. By formulating the problem as a maximum set coverage task for adiabatic quantum computers (AQC), R-QuMF outperforms existing quantum techniques, demonstrating superior performance across various synthetic and real-world 3D datasets. Our findings underscore the potential of quantum computing in addressing the complexities of MMF, especially in real-world scenarios with noisy and outlier-prone data.
Assistive XR research for disability at ACM ASSETS: A Scoping Review
Venue: arXiv cs.HC 2025
Year: 2025

Despite the rise in affordable eXtended Reality (XR) technologies, accessibility still remains a key concern, often excluding people with disabilities from accessing these immersive XR platforms. Consequently, there has been a notable surge in HCI research on creating accessible XR solutions (also known as, assistive XR). This increased focus in assistive XR research is also reflected in the number of research and innovative solutions submitted at the ACM Conference on Accessible Computing (ASSETS), with an aim to make XR experiences inclusive for disabled communities. However, till date, there is little to no work that provides a comprehensive overview of state-of-the-art research in assistive XR for disability at ACM ASSETS, a premier conference dedicated for research in HCI for people with disabilities. This study aims to fill this research gap by conducting a scoping review of literature delineating the key focus areas, research methods, statistical and temporal trends in XR research for disability at ACM ASSETS (2019-2023). From a pool of 1595 articles submitted to ASSETS, 26 articles are identified that specifically focus on XR research for disability. Through a detailed analysis, 6 key focus areas of XR research explored at ACM ASSETS are identified and a detailed examination of each is provided. Additionally, an overview of multiple research methods employed for XR research at ASSETS is also presented. Lastly, this work reports on the statistics and temporal trends regarding the number of publications, XR technologies used, disabilities addressed, and methodologies adopted for assistive XR research at ASSETS, highlighting emerging trends and possible future research directions.
Gamification as a Data Acquisition Strategy for Neurogames
Venue: arXiv cs.HC 2025
Year: 2025

The nascent field of neurogames relies on active Brain-Computer Interface input to drive its game mechanics. Consequently, users expect their conscious will to be meaningfully reflected on the virtual environment they're engaging in. Additionally, the videogame industry considers it paramount to provide gamers with seamless experiences to avoid disrupting their state of flow. Thus, this paper suggests gamification as a strategy to camouflage the often fatiguing data acquisition process in Machine Learning from neurodata so that neurogamers can further immerse themselves in the virtual experience while Artificial Intelligence models benefit from data taken in reproducible contexts.
Stakeholder perspectives on designing socially acceptable social robots and robot avatars for Dubai and multicultural societies
Venue: arXiv cs.HC 2025
Year: 2025

Robot avatars for customer service are gaining traction in Japan. However, their acceptance in other societal contexts remains underexplored, complicating efforts to design robot avatars suitable for diverse cultural environments. To address this, we interviewed key stakeholders in Dubai's service sector to gain insights into their experiences deploying social robots for customer service, as well as their opinions on the most useful tasks and design features that could maximize customer acceptance of robot avatars in Dubai. Providing information and guiding individuals to specific locations were identified as the most valued functions. Regarding appearance, robotic-looking, highly anthropomorphic designs were the most preferred. Ultra-realistic androids and cartoonish-looking robots elicited mixed reactions, while hybrid androids, low-anthropomorphic robotic designs, and animal-looking robots were considered less suitable or discouraged. Additionally, a psycho-sociological analysis revealed that interactions with robot avatars are influenced by their symbolic meaning, context, and affordances. These findings offer pioneering insights into culturally adaptive robot avatar design, addressing a significant research gap and providing actionable guidelines for deploying socially acceptable robots and avatars in multicultural contexts worldwide.
Bio-crafting Architecture: Experiences of growing mycelium in minimal surface molds
Venue: arXiv cs.HC 2025
Year: 2025

This study documents a three-week workshop with architecture students, where we designed and 3D printed various minimal surfaces using wood-based filaments, and used them as molds in which to grow mycelium. We detail the design process and the growth of the mycelium in different shapes, together with participants' experiences of working with a living material. After exhibiting the results of the work in a public-facing exhibition, we conducted interviews with members of the general public about their perceptions on interacting with a material such as mycelium in design. Our findings show that 3D-printed minimal surfaces with wood-based filaments can function as structural cores for mycelium-based composites and mycelium binds to the filament. Participants in the workshop exhibited stronger feelings for living materials compared to non-living ones, displaying both biophilia and, to a lesser extent, biophobia when interacting with the mycelium. Members of the general public discuss pragmatic aspects including mold, fragility, or production costs, and speculate on the future of bio-technology and its impact on everyday life. While all are positive about the impact on bio-technologies on the future, they have diverging opinions on how much ethical considerations should influence research directions.
Designing a Geo-Tourism App: A Principled Approach
Venue: arXiv cs.HC 2025
Year: 2025

Walking along trails in natural areas is a rewarding experience, but visitors sometimes need proper assistance to enhance their enjoyment, maximize learning, and ensure safety. Over the years, various signage techniques have been introduced, but today, the widespread use of smartphones offers new opportunities for visitor support. In this paper, we outline the key principles for designing an Android app tailored for geotourists. Our approach begins by defining user personas and deriving app requirements based on their needs. We then present a proof of concept that addresses the critical aspects identified during the design process.
New care pathways for supporting transitional care from hospitals to home using AI and personalized digital assistance
Venue: arXiv cs.HC 2025
Year: 2025

Transitional care may play a vital role for the sustainability of Europe future healthcare system, offering solutions for relocating patient care from hospital to home therefore addressing the growing demand for medical care as the population is ageing. However, to be effective, it is essential to integrate innovative Information and Communications Technology technologies to ensure that patients with comorbidities experience a smooth and coordinated transition from hospitals or care centers to home, thereby reducing the risk of rehospitalization. In this paper, we present an overview of the integration of Internet of Things, artificial intelligence, and digital assistance technologies with traditional care pathways to address the challenges and needs of healthcare systems in Europe. We identify the current gaps in transitional care and define the technology mapping to enhance the care pathways, aiming to improve patient outcomes, safety, and quality of life avoiding hospital readmissions. Finally, we define the trial setup and evaluation methodology needed to provide clinical evidence that supports the positive impact of technology integration on patient care and discuss the potential effects on the healthcare system.
Modeling the quantum-like dynamics of human reliability ratings in Human-AI interactions by interaction dependent Hamiltonians
Venue: arXiv cs.HC 2025
Year: 2025

As our information environments become ever more powered by artificial intelligence (AI), the phenomenon of trust in a human's interactions with this intelligence is becoming increasingly pertinent. For example, in the not too distant future, there will be teams of humans and intelligent robots involved in dealing with the repercussions of high-risk disaster situations such as hurricanes, earthquakes, or nuclear accidents. Even in such conditions of high uncertainty, humans and intelligent machines will need to engage in shared decision making, and trust is fundamental to the effectiveness of these interactions. A key challenge in modeling the dynamics of this trust is to provide a means to incorporate sensitivity to fluctuations in human trust judgments. In this article, we explore the ability of Quantum Random Walk models to model the dynamics of trust in human-AI interactions, and to integrate a sensitivity to fluctuations in participant trust judgments based on the nature of the interaction with the AI. We found that using empirical parameters to inform the use of different Hamiltonians can provide a promising means to model the evolution of trust in Human-AI interactions.